Comparison of Process Quality Characteristics Based on Change Request Data

  • Holger Schackmann
  • Horst Lichter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5338)


The evaluation of metrics on data available in change request management (CRM) systems offers valuable information for the assessment of process quality characteristics. The definition of appropriate metrics that consider the underlying change request workflow and address the information needs of an organization is an intricate task.

Furthermore CRM systems usually provide only a number of predefined metrics with limited adaptability. The tool BugzillaMetrics offers a more flexible approach that simplifies defining and adjusting new metrics. However a systematic approach for deriving an appropriate metric in a target-oriented way is needed. This paper describes a corresponding procedure on how to develop and validate metrics on CRM data applicable for the comparison of process quality characteristics.


Process Metrics Change Request Management Metric Specification Software Measurement Design Measurement Tool 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cook, J.E., Votta, L.G., Wolf, A.L.: Cost-Effective Analysis of In-Place Software Processes. IEEE Trans. on Software Engineering 24(8) (1998)Google Scholar
  2. 2.
    Grammel, L., Schackmann, H., Lichter, H.: BugzillaMetrics - Design of an adaptable tool for evaluating user-defined metric specifications on change requests. In: Büren, Bundschuh, Dumke (eds.) Tagungsband des DASMA Software Metrik Kongresses MetriKon 2007. Shaker Verlag, Aachen (2007)Google Scholar
  3. 3.
  4. 4.
    Gall, H.C., Lanza, M.: Software evolution: analysis and visualization. In: Proc.of the 28th international Conference on Software Engineering (ICSE 2006). ACM, New York (2006)Google Scholar
  5. 5.
    Canfora, G., Cerulo, L.: Impact Analysis by Mining Software and Change Request Repositories. In: Proc. of the 11th IEEE International Software Metrics Symposium – METRICS 2005, Como, Italy. IEEE CS Press, Los Alamitos (2005)Google Scholar
  6. 6.
    Kagdi, H.H., Collard, M.L., Maletic, J.I.: A survey and taxonomy of approaches for mining software repositories in the context of software evolution. Journal of Software Maintenance 19(2), 77–131 (2007)CrossRefGoogle Scholar
  7. 7.
    Sliwerski, J., Zimmermann, T., Zeller, A.: When do changes induce fixes? In: 2nd International Workshop on Mining Software Repositories (MSR 2005). ACM Press, New York (2005)Google Scholar
  8. 8.
    Koponen, T.: RaSOSS - Remote Analysis System for Open Source Software. In: The International Conference on Software Engineering Advances (ICSEA 2006). IEEE Press, Los Alamitos (2006)Google Scholar
  9. 9.
    Gasser, L., Ripoche, G.: Distributed Collective Practices and F/OSS Problem Management: Perspective and Methods. In: Conference on Cooperation, Innovation & Technology (CITE 2003), Troyes, France (2003)Google Scholar
  10. 10.
    ISO/IEC 15939. Systems and software engineering – Measurement Process. International Organization for Standardization – ISO, Geneva (2007)Google Scholar
  11. 11.
    Simon, F., Seng, O., Mohaupt, T.: Code-Quality Management. Dpunkt-Verlag, Heidelberg (2006)Google Scholar
  12. 12.
    Ebert, C., Dumke, R.: Software Measurement: Establish - Extract - Evaluate - Execute. Springer, Berlin (2007)CrossRefzbMATHGoogle Scholar
  13. 13.
    Gamma, E.: Agile, Open Source, Distributed, and On-Time – Inside the Eclipse Development Process. Keynote Talk, ICSE, St. Louis, Missouri (2005)Google Scholar
  14. 14.
    Basili, V., Caldiera, G., Rombach, H.D.: The Goal Question Metric Paradigm. In: Encyclopedia of Software Engineering, John Wiley & Sons, Chichester (1994)Google Scholar
  15. 15.
    ViPER - Visual Tooling Platform for Model-Based Engineering,
  16. 16.
    Eclipse Bugs – Remove LATER and REMIND resolutions,
  17. 17.
    Chillarege, R., Bhandari, I.S., Chaar, J.K., Halliday, M.J., Moebus, D.S., Ray, B.K., Wong, M.: Orthogonal Defect Classification - A Concept for In-Process Measurements. IEEE Trans. Software Eng. 18(11), 943–956 (1992)CrossRefGoogle Scholar
  18. 18.
    Makris, K., Ryu, K.D.: Scmbug: policy-based integration of software configuration management with bug-tracking. In: USENIX Annual Technical Conference. USENIX Association, Berkeley (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Holger Schackmann
    • 1
  • Horst Lichter
    • 1
  1. 1.Research Group Software ConstructionRWTH Aachen UniversityAachenGermany

Personalised recommendations